Parallel Ant Colony Optimization Algorithm Based Neural Method for Determining Resonant Frequencies of Various Microstrip Antennas

Central Force Optimization: Nelder-Mead Hybrid Algorithm for Rectangular Microstrip Antenna Design

“…In addition, the input resonant resistance of a probe-fed rectangular MSA is optimized to 50 by searching the appropriate feed-point position (y o ). The results are compared with the theoretical and experimental results reported by other scientists (Guney & Sarikaya, 2007;Gollapudi et al 2008Gollapudi et al , 2011Lohokare et al 2009;Kalinli et al, 2010). of a rectangular patch antenna driven at its fundamental TM 10 mode was given in Kara (1996) as…”

“…Therefore, in this case, the designers are forced to obtain the accurate resonant frequency using a trial-and-error method or by using numerical methods that usually require considerable computational time and costs. Recently, the resonant frequencies of both electrically thin and thick rectangular MSAs are obtained by using different algorithms (Guney & Sarikaya, 2007;Gollapudi et al 2008Gollapudi et al , 2011Lohokare et al 2009;Kalinli et al, 2010). In Guney and Sarikaya (2007), a combination of artificial neural networks with an adaptive-network based fuzzy interference system (ANFIS) is considered for calculating resonant frequency of a rectangular MSA.…”

“…The biogeography-based optimization (BBO) as a new bioinspired optimization technique was proposed by Lohokare et al (2009) for accurate determination of resonant frequency and feed-point calculation of a rectangular microstrip antenna. Kalinli et al (2010) presented a new approach based on feedforward artificial neural networks trained with a Levenberg-Marquardt parallel ant colony optimization (PACO) algorithm to determine the resonant frequencies of the rectangular, circular, and triangular MSAs. Gollapudi et al (2008) applied the bacterial foraging algorithm (BFA) to calculate the resonant frequency of a rectangular microstrip antenna.…”

Parallel metaheuristics: recent advances and new trends

“…Here, parallel metaheuristics provide a decisive help to tackle learning problems that handle large volume of data (Bouamama, ), and those control problems that involve complex training procedures (Hereford, , ; Huang et al., ). Bioinformatics, an emergent scientific field where parallel models of metaheuristics are helpful tools to cope with computationally expensive optimization problems in molecular biology that often also need to manage very large amount of data, such as sequence alignment (Gomes et al., ; Zola et al., ), DNA sequencing (Hongwei and Yanhua, ; Wirawan et al., ), gene finding (Rausch et al., ), genome assembly (Alba and Luque, ; Nebro et al., ), drug design (Boisson et al., ), protein structure alignment/prediction (Chu and Zomaya, ; Guo et al., ; Islam and Ngom, ; Tantar et al., ), phylogenetic inference (Blagojevic et al., ; Cancino et al., ; Grouchy et al., ), and other related problems (Guarracino et al., ; Martins et al., ; Nebro et al., ). Engineering design, where systems have many components, a large design space, and they usually involve functions with huge computation demands. These characteristics make parallel metaheuristics one of the most promising alternatives to get accurate solutions in reasonable execution times for complex tasks such as aerodynamic optimization and airfoil design (Asouti and Giannakoglou, ; Lim et al., ), design optimization of turbomachinery blade rows (Sasaki et al., ), electronic circuit and VLSI design (Alba et al., ; Lau et al., ; Sait et al., , ), antenna design (Kalinli et al., ; Weis and Lewis, ), signal processing (Li et al., ), etc. Hydraulic engineering, where parallel metaheuristics have been used to efficiently deal with real‐world scenarios arising in water supply network design optimization (López‐Ibánez, ), groundwater source identification (Babbar and Minsker, ; Mirghania et al., ; Sinha and Minsker, ), and multiobjective groundwater problems (Tang et al., ). Information processing, classification, and data mining, where parallel metaheuristics significantly help with the main challenge in this field, which is related to dealing with huge volumes of data, such as in feature selection and classification (Hamdani et al., ; Lopez et al., ), classification rules discovery (Chen ...…”

Full-wave Analysis of Rectangular Microstrip Antenna Printed on Electric-Magnetic Uniaxial Anisotropic Substrates

“…The features, including their very low profile, low cost, light weight, conformal structure, mechanical ruggedness, and ease in fabrication and integration with solid-state devices, make them important, especially for applications mounted on the exterior of aircraft and spacecraft or incorporated into mobile radio communications devices [1][2][3].…”